AI RESEARCH
Exploiting temporal parallelism for LSTM Autoencoder acceleration on FPGA
arXiv CS.LG
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ArXi:2603.13982v1 Announce Type: cross Recurrent Neural Networks (RNNs) are vital for sequential data processing. Long Short-Term Memory Autoencoders (LSTM-AEs) are particularly effective for unsupervised anomaly detection in time-series data. However, inherent sequential dependencies limit parallel computation. While previous work has explored FPGA-based acceleration for LSTM networks, efforts have typically focused on optimizing a single LSTM layer at a time. We